Accurate classification of images or features is an important field of study for a number of commercial applications. For example, the accurate classification of face images is essential for applications that involve automatic face authentication, e.g. for security systems and surveillance applications. The fundamental operation in such applications is face recognition, which involves the computation of similarities between face images. An unknown facial image is scanned, for example, by a security camera, and the unknown image is compared to a database of known images to find a match and identify the subject. The database may contain a number of varying images of the same subject to aid in recognition with different lighting, hair length, aging, facial hair, etc.
A number of approaches for face recognition, suitable for real-time applications, have been used in the prior art. These approaches can be classified as feature-based approaches, data or information-based approaches, such as Principal Components Analysis (PCA), Linear Discriminant Analysis (LDA), Enhanced FLD Model (EFM), and connectionist approaches employing artificial neural networks.
A facial image can be viewed as a two-dimensional collection of individual pixels. A straightforward method for comparing face images would be to compare each pixel of the unknown image to the pixel values of all of the images stored in the database and determine a distance measure based on pixel by pixel comparison. If the image is a monochrome gray-scale, then the distance measure is a comparison of the intensity of each pixel. The difference of each corresponding pixel intensity is measured and the differences are summed to attain a distance measure or a sum of absolute distances. The image with the least or minimum distance measure is the closest match. One drawback with pixel by pixel comparison between the unknown subject and every image in the database is the relatively long processing time needed to find the closest match. The pixel to pixel comparison also become complex if the images are misaligned, offset, or vary in size.
A histogram is another feature based classification method. The histogram separates the image into the number of pixels at varying intensity levels. For example, if the possible intensity levels falls between 0 and 255, then the histogram tracks the number of pixels with intensity levels between 0–5, and the number of pixels with intensity levels between 6–10, and so on up to 255. The histogram can be used to create a plot or distribution. The histogram plot of the unknown image is compared to the histogram plots of known images stored in the database to find the closest match in terms of similar distribution of data. One drawback with the histogram identification approach is that the comparison between plots does not distinguish different features or structure or content of the facial images.
Another technique for frontal face authentication has been proposed that involves extraction of feature vectors by employing multi-scale morphology followed by dimensionality reduction using PCA. This makes the technique both feature-based and data analysis-based. The multi-scale morphology employed in such techniques is performed with regular structuring elements. That is, the same generic structuring element is used at all points on a rectangular grid defined on the image.